Abstract
Left ventricle (LV) segmentation of 2-D echocardiography images is an essential step in the analysis of cardiac morphology and function and-more generally-diagnosis of cardiovascular diseases (CVD). Several deep learning (DL) algorithms have recently been proposed for the automatic segmentation of the LV, showing significant performance improvement over the traditional segmentation algorithms. However, unlike the traditional methods, prior information about the segmentation problem, e.g., anatomical shape information, is not usually incorporated for training the DL algorithms. This can degrade the generalization performance of the DL models on unseen images if their characteristics are somewhat different from those of the training images, e.g., low-quality testing images. In this study, a new shape-constrained deep convolutional neural network (CNN)-called B-spline explicit active surface (BEAS)-Net-is introduced for automatic LV segmentation. The BEAS-Net learns how to associate the image features, encoded by its convolutional layers, with anatomical shape-prior information derived by the BEAS algorithm to generate physiologically meaningful segmentation contours when dealing with artifactual or low-quality images. The performance of the proposed network was evaluated using three different in vivo datasets and was compared with a deep segmentation algorithm based on the U-Net model. Both the networks yielded comparable results when tested on images of acceptable quality, but the BEAS-Net outperformed the benchmark DL model on artifactual and low-quality images.

Abstract
Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) in adults can present challenges in learning and work environments, by impacting focus, organization, social interaction, and self-esteem. The aim of this study is the potential of Human-Computer Interaction (HCI) in the empowerment of adults with ADHD and ASD. Specific difficulties faced in educational and professional settings were found through qualitative interviews with six participants. HCI seems to offer a pathway towards a more inclusive future, as educational technology solutions built on HCI principles can create better and alternative learning environments with fewer distractions and gamification for increased engagement. Assistive technologies can address challenges related to focus and organization (like task management apps, time tracking tools). Additionally, features promoting social interaction and communication can empower individuals with ASD. Technologies arising nowadays like Augmented and Virtual Reality (AR/VR) can create interactive learning experiences. Through the use of Human-Computer Interaction principles, more inclusive learning and work environments that empower individuals with ADHD and ASD can originate, while improving engagement and efficiency for all. © 2025 Elsevier B.V., All rights reserved.

Abstract
Sectorization problems refer to dividing a large set, area or network into smaller parts concerning one or more objectives. A decision support system (DSS) is a relevant tool for solving these problems, improving optimisation procedures, and finding feasible solutions more efficiently. This paper presents a new web-based Decision Support System for Sectorization (D3S). D3S is designed to solve sectorization problems in various areas, such as school and health districting,planning sales territories and maintenance operations zones, or political districting. Due to its generic design, D3S bridges the gap between sectorization problems and a state-of-the-art decision support tool. The paper aims to present the generic and technical attributes of D3S by providing detailed information regarding the problem-solution approach (based on Evolutionary Algorithms), objectives (most common in sectorization), constraints, structure and performance.

Abstract
The authors present the Effective Solution Based Learning, which derives from Project-Based Learning but is applied to real problems in order to build effective solutions. Emphasis is placed on the effectiveness on the assumption that encourages greater involvement and commitment on the part of students, ensuring a context that is intended to be more attractive and close to what will be their professional reality. Effectiveness is measured by the functionalities considered essential for solving the problem, but also the viability of the solution to be effectively used after the end of development, without the need for continued student involvement. A brief summary of the methodology is presented in the paper, emphasizing, in particular, the criteria and requirements for choosing project themes. The results of the first year of evaluation are also presented in the paper, pointing to a clear reduction in dropouts and an increase in approved students. Considering that this is achieved with more demand and work, it is arguable that it also resulted in students with more knowledge and skills. The authors also include in this paper the results of a survey done to the students, after the project conclusion, to assess the students’ perspective on this methodology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
Recently, the uptake of renewable energy has surged in distribution networks, particularly due to the costeffectiveness and modular nature of photovoltaic systems. This has paved the way to a new era of user engagement, embodied by individual and collective self-consumption, and promoted by the EU Directive 2018/ 2001, which advocates for the establishment of Renewable Energy Communities. However, the transposition of this directive varies across Member States, resulting in specific rules for each country. In this work, the impact that different energy sharing models have on the same community is quantitatively assessed. The policy analysis focuses on the regulation of two countries, Italy and Portugal, chosen for the specular ways in which their models operate, respectively virtually and physically. The analysis is supported by a suite of tools which includes two optimization problems for community's operations, one for each analysed regulation, and a set of consumer protection mechanisms, to ensure no member is losing money while in community. Results demonstrate that the sharing model impacts community's optimal operations, optimal battery size and configuration, and members' benefit. As these models are sensitive to different variables, personalized interventions at national level are required.

Abstract

Background Subcutaneous (SAF) and visceral (VAF) abdominal fat have specific properties which the global body fat and total abdominal fat (TAF) size metrics do not capture. Beyond size, radiomics allows deep tissue phenotyping and may capture fat dysfunction. We aimed to characterize the computed tomography (CT) radiomics of SAF and VAF and assess their incremental value above fat size to detect coronary calcification. Methods SAF, VAF and TAF area, signal distribution and texture were extracted from non-contrast CT of 1001 subjects (57% male, 57?±?10 years) with no established cardiovascular disease who underwent CT for coronary calcium score (CCS) with additional abdominal slice (L4/5-S1). XGBoost machine learning models (ML) were used to identify the best features that discriminate SAF from VAF and to train/test ML to detect any coronary calcification (CCS?>?0). Results SAF and VAF appearance in non-contrast CT differs: SAF displays brighter and finer texture than VAF. Compared with CCS?=?0, SAF of CCS?>?0 has higher signal and homogeneous texture, while VAF of CCS?>?0 has lower signal and heterogeneous texture. SAF signal/texture improved SAF area performance to detect CCS?>?0. A ML including SAF and VAF area performed better than TAF area to discriminate CCS?>?0 from CCS?=?0, however, a combined ML of the best SAF and VAF features detected CCS?>?0 as the best TAF features. Conclusion In non-contrast CT, SAF and VAF appearance differs and SAF radiomics improves the detection of CCS?>?0 when added to fat area; TAF radiomics (but not TAF area) spares the need for separate SAF and VAF segmentations.